class_docstr: Provides methods for retrieving daily stock data from
  Yahoo Finance API
code: def calculate_turbulence(
      self, data: pd.DataFrame, time_period: int = 252
  ) -> pd.DataFrame:
      # can add other market assets
      df = data.copy()
      df_price_pivot = df.pivot(
          index="timestamp", columns="tic", values="close"
      )
      # use returns to calculate turbulence
      df_price_pivot = df_price_pivot.pct_change()
  
      unique_date = df.timestamp.unique()
      # start after a fixed timestamp period
      start = time_period
      turbulence_index = [0] * start
      # turbulence_index = [0]
      count = 0
      for i in range(start, len(unique_date)):
          current_price = df_price_pivot[
              df_price_pivot.index == unique_date[i]
          ]
          # use one year rolling window to calcualte covariance
          hist_price = df_price_pivot[
              (df_price_pivot.index < unique_date[i])
              & (df_price_pivot.index >= unique_date[i - time_period])
          ]
          # Drop tickers which has number missing values more than the "oldest" ticker
          filtered_hist_price = hist_price.iloc[
              hist_price.isna().sum().min() :
          ].dropna(axis=1)
  
          cov_temp = filtered_hist_price.cov()
          current_temp = current_price[
              [x for x in filtered_hist_price]
          ] - np.mean(filtered_hist_price, axis=0)
          temp = current_temp.values.dot(np.linalg.pinv(cov_temp)).dot(
              current_temp.values.T
          )
          if temp > 0:
              count += 1
              if count > 2:
                  turbulence_temp = temp[0][0]
              else:
                  # avoid large outlier because of the calculation just begins
                  turbulence_temp = 0
          else:
              turbulence_temp = 0
          turbulence_index.append(turbulence_temp)
  
      turbulence_index = pd.DataFrame(
          {
              "timestamp": df_price_pivot.index,
              "turbulence": turbulence_index,
          }
      )
      return turbulence_index
  
imports: from __future__ import annotations
  import datetime
  from datetime import date
  from datetime import timedelta
  from sqlite3 import Timestamp
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Type
  from typing import TypeVar
  from typing import Union
  import exchange_calendars
  import numpy
  import pandas
  import pytz
  import yfinance
  from stockstats import StockDataFrame
label_desc: Use image node_id 7 for calling the YahooFinanceProcessor obj's underlying member method code with example usage: obj.calculate_turbulence(data, time_period) and returns: turbulence_index